Overview

Dataset statistics

Number of variables17
Number of observations13611
Missing cells0
Missing cells (%)0.0%
Duplicate rows68
Duplicate rows (%)0.5%
Total size in memory1.8 MiB
Average record size in memory136.0 B

Variable types

Numeric16
Categorical1

Alerts

Dataset has 68 (0.5%) duplicate rowsDuplicates
Area is highly correlated with Perimeter and 12 other fieldsHigh correlation
Perimeter is highly correlated with Area and 12 other fieldsHigh correlation
MajorAxisLength is highly correlated with Area and 12 other fieldsHigh correlation
MinorAxisLength is highly correlated with Area and 5 other fieldsHigh correlation
AspectRation is highly correlated with Area and 10 other fieldsHigh correlation
Eccentricity is highly correlated with Area and 10 other fieldsHigh correlation
ConvexArea is highly correlated with Area and 12 other fieldsHigh correlation
EquivDiameter is highly correlated with Area and 12 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Area and 11 other fieldsHigh correlation
Compactness is highly correlated with Area and 10 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 5 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 10 other fieldsHigh correlation
ShapeFactor3 is highly correlated with Area and 10 other fieldsHigh correlation
ShapeFactor4 is highly correlated with Area and 11 other fieldsHigh correlation
Area is highly correlated with Perimeter and 6 other fieldsHigh correlation
Perimeter is highly correlated with Area and 7 other fieldsHigh correlation
MajorAxisLength is highly correlated with Area and 11 other fieldsHigh correlation
MinorAxisLength is highly correlated with Area and 5 other fieldsHigh correlation
AspectRation is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
Eccentricity is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
ConvexArea is highly correlated with Area and 6 other fieldsHigh correlation
EquivDiameter is highly correlated with Area and 6 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Perimeter and 7 other fieldsHigh correlation
Compactness is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 5 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 10 other fieldsHigh correlation
ShapeFactor3 is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
ShapeFactor4 is highly correlated with Solidity and 1 other fieldsHigh correlation
Area is highly correlated with Perimeter and 6 other fieldsHigh correlation
Perimeter is highly correlated with Area and 7 other fieldsHigh correlation
MajorAxisLength is highly correlated with Area and 10 other fieldsHigh correlation
MinorAxisLength is highly correlated with Area and 5 other fieldsHigh correlation
AspectRation is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
Eccentricity is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
ConvexArea is highly correlated with Area and 6 other fieldsHigh correlation
EquivDiameter is highly correlated with Area and 6 other fieldsHigh correlation
roundness is highly correlated with Perimeter and 6 other fieldsHigh correlation
Compactness is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 4 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 9 other fieldsHigh correlation
ShapeFactor3 is highly correlated with MajorAxisLength and 5 other fieldsHigh correlation
Area is highly correlated with Perimeter and 12 other fieldsHigh correlation
Perimeter is highly correlated with Area and 12 other fieldsHigh correlation
MajorAxisLength is highly correlated with Area and 13 other fieldsHigh correlation
MinorAxisLength is highly correlated with Area and 12 other fieldsHigh correlation
AspectRation is highly correlated with Area and 14 other fieldsHigh correlation
Eccentricity is highly correlated with Area and 13 other fieldsHigh correlation
ConvexArea is highly correlated with Area and 12 other fieldsHigh correlation
EquivDiameter is highly correlated with Area and 12 other fieldsHigh correlation
Extent is highly correlated with AspectRation and 4 other fieldsHigh correlation
Solidity is highly correlated with roundness and 1 other fieldsHigh correlation
roundness is highly correlated with Area and 15 other fieldsHigh correlation
Compactness is highly correlated with Area and 14 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 12 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 13 other fieldsHigh correlation
ShapeFactor3 is highly correlated with Area and 14 other fieldsHigh correlation
ShapeFactor4 is highly correlated with MajorAxisLength and 6 other fieldsHigh correlation
Class is highly correlated with Area and 12 other fieldsHigh correlation

Reproduction

Analysis started2022-06-17 17:08:44.896776
Analysis finished2022-06-17 17:09:45.946407
Duration1 minute and 1.05 second
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12011
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53048.28455
Minimum20420
Maximum254616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:46.147688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20420
5-th percentile27660.5
Q136328
median44652
Q361332
95-th percentile89824.5
Maximum254616
Range234196
Interquartile range (IQR)25004

Descriptive statistics

Standard deviation29324.09572
Coefficient of variation (CV)0.552781225
Kurtosis10.80081397
Mean53048.28455
Median Absolute Deviation (MAD)10386
Skewness2.952930971
Sum722040201
Variance859902589.6
MonotonicityNot monotonic
2022-06-17T22:09:46.371190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
354424
 
< 0.1%
384264
 
< 0.1%
347744
 
< 0.1%
405044
 
< 0.1%
522664
 
< 0.1%
345944
 
< 0.1%
335184
 
< 0.1%
281224
 
< 0.1%
382734
 
< 0.1%
361094
 
< 0.1%
Other values (12001)13571
99.7%
ValueCountFrequency (%)
204201
< 0.1%
204641
< 0.1%
205481
< 0.1%
207111
< 0.1%
207861
< 0.1%
209421
< 0.1%
211011
< 0.1%
213141
< 0.1%
213481
< 0.1%
213971
< 0.1%
ValueCountFrequency (%)
2546161
< 0.1%
2514321
< 0.1%
2484241
< 0.1%
2413221
< 0.1%
2372701
< 0.1%
2348981
< 0.1%
2337511
< 0.1%
2310661
< 0.1%
2308671
< 0.1%
2268061
< 0.1%

Perimeter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13351
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.2834586
Minimum524.736
Maximum1985.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:46.589902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum524.736
5-th percentile617.737
Q1703.5235
median794.941
Q3977.213
95-th percentile1181.124
Maximum1985.37
Range1460.634
Interquartile range (IQR)273.6895

Descriptive statistics

Standard deviation214.2896959
Coefficient of variation (CV)0.2505481589
Kurtosis3.588123327
Mean855.2834586
Median Absolute Deviation (MAD)117.877
Skewness1.626123524
Sum11641263.16
Variance45920.07377
MonotonicityNot monotonic
2022-06-17T22:09:46.804745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701.6443
 
< 0.1%
683.3413
 
< 0.1%
911.5893
 
< 0.1%
984.1233
 
< 0.1%
805.1683
 
< 0.1%
984.1522
 
< 0.1%
740.0172
 
< 0.1%
930.032
 
< 0.1%
639.0392
 
< 0.1%
732.1032
 
< 0.1%
Other values (13341)13586
99.8%
ValueCountFrequency (%)
524.7361
< 0.1%
524.9321
< 0.1%
525.4131
< 0.1%
528.4081
< 0.1%
530.6831
< 0.1%
530.8251
< 0.1%
531.3181
< 0.1%
533.7011
< 0.1%
534.7171
< 0.1%
534.9181
< 0.1%
ValueCountFrequency (%)
1985.371
< 0.1%
1921.6851
< 0.1%
1919.8681
< 0.1%
1895.941
< 0.1%
1884.5571
< 0.1%
1869.8851
< 0.1%
1849.6991
< 0.1%
1849.3711
< 0.1%
1847.941
< 0.1%
1845.8551
< 0.1%

MajorAxisLength
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.1418673
Minimum183.601165
Maximum738.8601535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:47.067431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum183.601165
5-th percentile224.6446749
Q1253.3036327
median296.8833669
Q3376.4950124
95-th percentile448.3239443
Maximum738.8601535
Range555.2589885
Interquartile range (IQR)123.1913797

Descriptive statistics

Standard deviation85.69418596
Coefficient of variation (CV)0.267675661
Kurtosis2.531902062
Mean320.1418673
Median Absolute Deviation (MAD)54.97424343
Skewness1.357815284
Sum4357450.956
Variance7343.493507
MonotonicityNot monotonic
2022-06-17T22:09:47.271790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306.53388612
 
< 0.1%
365.74505342
 
< 0.1%
337.17146392
 
< 0.1%
347.44275512
 
< 0.1%
352.48208892
 
< 0.1%
365.29809562
 
< 0.1%
398.37676622
 
< 0.1%
404.36942132
 
< 0.1%
349.45945032
 
< 0.1%
396.83437052
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
183.6011651
< 0.1%
183.96525151
< 0.1%
185.38192141
< 0.1%
186.07914941
< 0.1%
187.16863481
< 0.1%
188.91916041
< 0.1%
189.79938491
< 0.1%
190.28263211
< 0.1%
191.17652531
< 0.1%
191.20291341
< 0.1%
ValueCountFrequency (%)
738.86015351
< 0.1%
738.14450171
< 0.1%
726.37349321
< 0.1%
722.49406831
< 0.1%
721.21609841
< 0.1%
720.69552051
< 0.1%
719.12569041
< 0.1%
715.05303981
< 0.1%
713.96728211
< 0.1%
713.3668371
< 0.1%

MinorAxisLength
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.2707141
Minimum122.5126535
Maximum460.1984968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:47.493435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum122.5126535
5-th percentile153.8136273
Q1175.84817
median192.4317333
Q3217.0317413
95-th percentile267.8851765
Maximum460.1984968
Range337.6858434
Interquartile range (IQR)41.18357126

Descriptive statistics

Standard deviation44.97009129
Coefficient of variation (CV)0.2223262596
Kurtosis6.651066804
Mean202.2707141
Median Absolute Deviation (MAD)19.06314747
Skewness2.23821054
Sum2753106.689
Variance2022.309111
MonotonicityNot monotonic
2022-06-17T22:09:47.732298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160.59178442
 
< 0.1%
198.05640982
 
< 0.1%
160.03606742
 
< 0.1%
172.12879092
 
< 0.1%
189.30095142
 
< 0.1%
175.48638362
 
< 0.1%
182.08551772
 
< 0.1%
197.14576392
 
< 0.1%
202.93473142
 
< 0.1%
196.17121292
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
122.51265351
< 0.1%
129.57620691
< 0.1%
129.74819351
< 0.1%
130.73668951
< 0.1%
131.43305861
< 0.1%
132.14355311
< 0.1%
132.18752071
< 0.1%
132.2983361
< 0.1%
132.30796931
< 0.1%
132.64021861
< 0.1%
ValueCountFrequency (%)
460.19849681
< 0.1%
450.92618671
< 0.1%
449.33967841
< 0.1%
447.41832871
< 0.1%
446.04361761
< 0.1%
440.6864181
< 0.1%
435.83785651
< 0.1%
432.58941991
< 0.1%
432.38982131
< 0.1%
429.96212821
< 0.1%

AspectRation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.583241979
Minimum1.024867596
Maximum2.430306447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:48.006961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.024867596
5-th percentile1.208046988
Q11.432306932
median1.551123666
Q31.707108927
95-th percentile2.082011458
Maximum2.430306447
Range1.405438851
Interquartile range (IQR)0.2748019952

Descriptive statistics

Standard deviation0.2466784557
Coefficient of variation (CV)0.155805909
Kurtosis0.1138144406
Mean1.583241979
Median Absolute Deviation (MAD)0.1354228505
Skewness0.5825733992
Sum21549.50658
Variance0.0608502605
MonotonicityNot monotonic
2022-06-17T22:09:48.241316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9087768862
 
< 0.1%
1.8466711272
 
< 0.1%
2.1068467212
 
< 0.1%
2.0185045942
 
< 0.1%
1.8620196372
 
< 0.1%
2.0816321372
 
< 0.1%
2.1878553072
 
< 0.1%
2.0511189962
 
< 0.1%
1.7220287912
 
< 0.1%
2.0228980832
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
1.0248675961
< 0.1%
1.0364226811
< 0.1%
1.0419636991
< 0.1%
1.0471596121
< 0.1%
1.0491801211
< 0.1%
1.0494240051
< 0.1%
1.0607856871
< 0.1%
1.060798021
< 0.1%
1.0646977611
< 0.1%
1.0652472051
< 0.1%
ValueCountFrequency (%)
2.4303064471
< 0.1%
2.3888734361
< 0.1%
2.3873948861
< 0.1%
2.3832604811
< 0.1%
2.3640166091
< 0.1%
2.3582717561
< 0.1%
2.3504731911
< 0.1%
2.3476662411
< 0.1%
2.3450777041
< 0.1%
2.343293241
< 0.1%

Eccentricity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7508949294
Minimum0.2189512634
Maximum0.9114229685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:48.489995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2189512634
5-th percentile0.5610493263
Q10.7159277459
median0.7644408064
Q30.8104659845
95-th percentile0.8771016696
Maximum0.9114229685
Range0.6924717051
Interquartile range (IQR)0.09453823854

Descriptive statistics

Standard deviation0.0920017632
Coefficient of variation (CV)0.1225228186
Kurtosis1.387455596
Mean0.7508949294
Median Absolute Deviation (MAD)0.04716201811
Skewness-1.062823931
Sum10220.43088
Variance0.008464324433
MonotonicityNot monotonic
2022-06-17T22:09:48.786847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85178242352
 
< 0.1%
0.84069112782
 
< 0.1%
0.88017841622
 
< 0.1%
0.86865570152
 
< 0.1%
0.84354960712
 
< 0.1%
0.8770537392
 
< 0.1%
0.88943143592
 
< 0.1%
0.87310134762
 
< 0.1%
0.81411022012
 
< 0.1%
0.86926847982
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.21895126341
< 0.1%
0.26277437511
< 0.1%
0.28093658761
< 0.1%
0.29672086111
< 0.1%
0.30257623321
< 0.1%
0.3032730941
< 0.1%
0.33364869421
< 0.1%
0.33367965781
< 0.1%
0.3432784411
< 0.1%
0.34460103161
< 0.1%
ValueCountFrequency (%)
0.91142296851
< 0.1%
0.90816731881
< 0.1%
0.90804777631
< 0.1%
0.90771223871
< 0.1%
0.90612553981
< 0.1%
0.90564376981
< 0.1%
0.9049836961
< 0.1%
0.90474438431
< 0.1%
0.90452287541
< 0.1%
0.9043697141
< 0.1%

ConvexArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12066
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53768.20021
Minimum20684
Maximum263261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:49.035599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20684
5-th percentile28011.5
Q136714.5
median45178
Q362294
95-th percentile91282
Maximum263261
Range242577
Interquartile range (IQR)25579.5

Descriptive statistics

Standard deviation29774.91582
Coefficient of variation (CV)0.5537644129
Kurtosis10.74364015
Mean53768.20021
Median Absolute Deviation (MAD)10566
Skewness2.941821111
Sum731838973
Variance886545611.9
MonotonicityNot monotonic
2022-06-17T22:09:49.238689image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
370235
 
< 0.1%
389414
 
< 0.1%
431984
 
< 0.1%
478884
 
< 0.1%
417064
 
< 0.1%
314234
 
< 0.1%
474464
 
< 0.1%
452434
 
< 0.1%
477294
 
< 0.1%
442624
 
< 0.1%
Other values (12056)13570
99.7%
ValueCountFrequency (%)
206841
< 0.1%
207721
< 0.1%
208251
< 0.1%
209881
< 0.1%
210571
< 0.1%
211911
< 0.1%
214621
< 0.1%
215871
< 0.1%
215901
< 0.1%
217311
< 0.1%
ValueCountFrequency (%)
2632611
< 0.1%
2574251
< 0.1%
2510821
< 0.1%
2443191
< 0.1%
2406711
< 0.1%
2382651
< 0.1%
2373441
< 0.1%
2332431
< 0.1%
2329031
< 0.1%
2315001
< 0.1%

EquivDiameter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12011
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.0642199
Minimum161.2437642
Maximum569.3743583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:49.534268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum161.2437642
5-th percentile187.6657731
Q1215.0680034
median238.4380258
Q3279.4464667
95-th percentile338.1835379
Maximum569.3743583
Range408.1305941
Interquartile range (IQR)64.37846327

Descriptive statistics

Standard deviation59.17712015
Coefficient of variation (CV)0.2338423036
Kurtosis5.192057256
Mean253.0642199
Median Absolute Deviation (MAD)28.34121827
Skewness1.94895761
Sum3444457.097
Variance3501.931549
MonotonicityNot monotonic
2022-06-17T22:09:50.019122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212.42917874
 
< 0.1%
221.19110014
 
< 0.1%
210.41775574
 
< 0.1%
227.09314064
 
< 0.1%
257.96731974
 
< 0.1%
209.87245844
 
< 0.1%
206.58277534
 
< 0.1%
189.22484644
 
< 0.1%
220.75030494
 
< 0.1%
214.41876494
 
< 0.1%
Other values (12001)13571
99.7%
ValueCountFrequency (%)
161.24376421
< 0.1%
161.41739081
< 0.1%
161.74834211
< 0.1%
162.38862091
< 0.1%
162.68238131
< 0.1%
163.291711
< 0.1%
163.91042561
< 0.1%
164.73562961
< 0.1%
164.866971
< 0.1%
165.05607091
< 0.1%
ValueCountFrequency (%)
569.37435831
< 0.1%
565.80311521
< 0.1%
562.40844651
< 0.1%
554.31102591
< 0.1%
549.63765041
< 0.1%
546.8833721
< 0.1%
545.54653041
< 0.1%
542.40424841
< 0.1%
542.17063181
< 0.1%
537.3810271
< 0.1%

Extent
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13535
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7497327874
Minimum0.5553147168
Maximum0.8661946406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:50.269125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5553147168
5-th percentile0.6551083029
Q10.7186335273
median0.759858948
Q30.7868514966
95-th percentile0.8122529084
Maximum0.8661946406
Range0.3108799238
Interquartile range (IQR)0.06821796925

Descriptive statistics

Standard deviation0.04908636685
Coefficient of variation (CV)0.06547181566
Kurtosis0.643318844
Mean0.7497327874
Median Absolute Deviation (MAD)0.03207474095
Skewness-0.8953484282
Sum10204.61297
Variance0.00240947141
MonotonicityNot monotonic
2022-06-17T22:09:50.524449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.70882497542
 
< 0.1%
0.74950526562
 
< 0.1%
0.81905901122
 
< 0.1%
0.72592061532
 
< 0.1%
0.75763013862
 
< 0.1%
0.78756996722
 
< 0.1%
0.60141952612
 
< 0.1%
0.62727188212
 
< 0.1%
0.78771184282
 
< 0.1%
0.78936507942
 
< 0.1%
Other values (13525)13591
99.9%
ValueCountFrequency (%)
0.55531471681
< 0.1%
0.56666925381
< 0.1%
0.56676752681
< 0.1%
0.57023761981
< 0.1%
0.57027496061
< 0.1%
0.57220885421
< 0.1%
0.57237913461
< 0.1%
0.57336734691
< 0.1%
0.57404036411
< 0.1%
0.57441768651
< 0.1%
ValueCountFrequency (%)
0.86619464061
< 0.1%
0.85841985051
< 0.1%
0.85284142611
< 0.1%
0.85226860251
< 0.1%
0.85194805191
< 0.1%
0.85074422821
< 0.1%
0.85025131121
< 0.1%
0.84862264221
< 0.1%
0.84797499031
< 0.1%
0.84636246531
< 0.1%

Solidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13526
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9871428436
Minimum0.9192461571
Maximum0.9946774999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:50.758806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.9192461571
5-th percentile0.9784049827
Q10.9856703983
median0.9882829985
Q30.99001311
95-th percentile0.9920273149
Maximum0.9946774999
Range0.07543134286
Interquartile range (IQR)0.004342711665

Descriptive statistics

Standard deviation0.004660379163
Coefficient of variation (CV)0.004721078812
Kurtosis12.79962097
Mean0.9871428436
Median Absolute Deviation (MAD)0.00202907936
Skewness-2.550093108
Sum13436.00124
Variance2.171913395 × 10-5
MonotonicityNot monotonic
2022-06-17T22:09:51.040036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98669127652
 
< 0.1%
0.98478960342
 
< 0.1%
0.99077340572
 
< 0.1%
0.98830889332
 
< 0.1%
0.98890649762
 
< 0.1%
0.98901788522
 
< 0.1%
0.99154179592
 
< 0.1%
0.99061761732
 
< 0.1%
0.95663408612
 
< 0.1%
0.98083276592
 
< 0.1%
Other values (13516)13591
99.9%
ValueCountFrequency (%)
0.91924615711
< 0.1%
0.94355930061
< 0.1%
0.9445680681
< 0.1%
0.94663368091
< 0.1%
0.94671909561
< 0.1%
0.94766676371
< 0.1%
0.94902320541
< 0.1%
0.94910252881
< 0.1%
0.95194751671
< 0.1%
0.9532961081
< 0.1%
ValueCountFrequency (%)
0.99467749991
< 0.1%
0.99437818231
< 0.1%
0.99425963261
< 0.1%
0.99421348451
< 0.1%
0.99408217951
< 0.1%
0.99408177331
< 0.1%
0.99404917521
< 0.1%
0.99394544061
< 0.1%
0.99391933311
< 0.1%
0.99390551141
< 0.1%

roundness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8732818313
Minimum0.4896182562
Maximum0.9906853996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:51.274392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4896182562
5-th percentile0.7709554641
Q10.832096329
median0.8831572892
Q30.916868833
95-th percentile0.9592536083
Maximum0.9906853996
Range0.5010671434
Interquartile range (IQR)0.084772504

Descriptive statistics

Standard deviation0.05951988795
Coefficient of variation (CV)0.06815656277
Kurtosis0.3743063332
Mean0.8732818313
Median Absolute Deviation (MAD)0.03983167836
Skewness-0.6357489499
Sum11886.23901
Variance0.003542617061
MonotonicityNot monotonic
2022-06-17T22:09:51.508775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84417352792
 
< 0.1%
0.80116522352
 
< 0.1%
0.78096521832
 
< 0.1%
0.78265086742
 
< 0.1%
0.81751203442
 
< 0.1%
0.81256201352
 
< 0.1%
0.77486343262
 
< 0.1%
0.78929690862
 
< 0.1%
0.78776595382
 
< 0.1%
0.78880893172
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.48961825621
< 0.1%
0.55676582611
< 0.1%
0.57180091031
< 0.1%
0.57602866191
< 0.1%
0.57784359441
< 0.1%
0.59370827981
< 0.1%
0.59504840071
< 0.1%
0.6019785431
< 0.1%
0.60539939611
< 0.1%
0.606569671
< 0.1%
ValueCountFrequency (%)
0.99068539961
< 0.1%
0.98791973481
< 0.1%
0.98708924371
< 0.1%
0.98681228461
< 0.1%
0.98668473121
< 0.1%
0.9861149611
< 0.1%
0.98610296271
< 0.1%
0.98594949751
< 0.1%
0.98496588111
< 0.1%
0.98487706941
< 0.1%

Compactness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7998636819
Minimum0.640576759
Maximum0.9873029694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:51.743109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.640576759
5-th percentile0.6905667073
Q10.762468747
median0.801276684
Q30.8342699108
95-th percentile0.9089897503
Maximum0.9873029694
Range0.3467262104
Interquartile range (IQR)0.07180116379

Descriptive statistics

Standard deviation0.06171346312
Coefficient of variation (CV)0.07715497593
Kurtosis-0.2234594687
Mean0.7998636819
Median Absolute Deviation (MAD)0.03572565764
Skewness0.03711545824
Sum10886.94457
Variance0.00380855153
MonotonicityNot monotonic
2022-06-17T22:09:52.164957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72159714862
 
< 0.1%
0.72448545142
 
< 0.1%
0.68566995452
 
< 0.1%
0.703051022
 
< 0.1%
0.73218835752
 
< 0.1%
0.69195413192
 
< 0.1%
0.67481709582
 
< 0.1%
0.69637645892
 
< 0.1%
0.75144568992
 
< 0.1%
0.70109381982
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.6405767591
< 0.1%
0.64536765851
< 0.1%
0.64579636261
< 0.1%
0.6465620191
< 0.1%
0.64876195991
< 0.1%
0.64903870391
< 0.1%
0.64918947921
< 0.1%
0.65163080291
< 0.1%
0.651870981
< 0.1%
0.65214801811
< 0.1%
ValueCountFrequency (%)
0.98730296941
< 0.1%
0.98161114261
< 0.1%
0.9794319921
< 0.1%
0.97673884051
< 0.1%
0.97589475961
< 0.1%
0.97559450711
< 0.1%
0.97051552321
< 0.1%
0.97047143361
< 0.1%
0.9687330661
< 0.1%
0.96844399391
< 0.1%

ShapeFactor1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00656360851
Minimum0.002778012668
Maximum0.01045116932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:52.399336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.002778012668
5-th percentile0.004781539009
Q10.005899917412
median0.006645174459
Q30.007271420159
95-th percentile0.008305289248
Maximum0.01045116932
Range0.007673156656
Interquartile range (IQR)0.001371502748

Descriptive statistics

Standard deviation0.001127998228
Coefficient of variation (CV)0.1718564149
Kurtosis0.7143548169
Mean0.00656360851
Median Absolute Deviation (MAD)0.0006770317251
Skewness-0.534140543
Sum89.33727543
Variance1.272380001 × 10-6
MonotonicityNot monotonic
2022-06-17T22:09:52.602446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0079770444252
 
< 0.1%
0.0066324245792
 
< 0.1%
0.0080320992872
 
< 0.1%
0.0074140100962
 
< 0.1%
0.0067379444672
 
< 0.1%
0.0072796097252
 
< 0.1%
0.0070184944982
 
< 0.1%
0.0064929737842
 
< 0.1%
0.0064523532182
 
< 0.1%
0.0065275252582
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.0027780126681
< 0.1%
0.002855716381
< 0.1%
0.0028601199941
< 0.1%
0.0028783573241
< 0.1%
0.0029018606591
< 0.1%
0.0029091008761
< 0.1%
0.0029424546661
< 0.1%
0.0029593169361
< 0.1%
0.0029744233571
< 0.1%
0.0029849434351
< 0.1%
ValueCountFrequency (%)
0.010451169321
< 0.1%
0.0098970034311
< 0.1%
0.0098239799591
< 0.1%
0.009747873631
< 0.1%
0.009720055881
< 0.1%
0.00966914951
< 0.1%
0.0096664697811
< 0.1%
0.0096615868881
< 0.1%
0.0096529594661
< 0.1%
0.0096123112291
< 0.1%

ShapeFactor2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001715947334
Minimum0.000564169018
Maximum0.003664971964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:52.821181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.000564169018
5-th percentile0.0009112717192
Q10.001153520228
median0.001693530977
Q30.002170268301
95-th percentile0.002724234221
Maximum0.003664971964
Range0.003100802946
Interquartile range (IQR)0.001016748073

Descriptive statistics

Standard deviation0.0005958748567
Coefficient of variation (CV)0.3472570776
Kurtosis-0.8592542148
Mean0.001715947334
Median Absolute Deviation (MAD)0.0005128990973
Skewness0.3012259297
Sum23.35575917
Variance3.550668449 × 10-7
MonotonicityNot monotonic
2022-06-17T22:09:53.024267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0013341387772
 
< 0.1%
0.0011271215612
 
< 0.1%
0.001095139162
 
< 0.1%
0.0011173270332
 
< 0.1%
0.0011945338632
 
< 0.1%
0.0010294306322
 
< 0.1%
0.00089777608472
 
< 0.1%
0.00094188902722
 
< 0.1%
0.0012690779182
 
< 0.1%
0.00097282086972
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.0005641690181
< 0.1%
0.00056698851191
< 0.1%
0.00056784292821
< 0.1%
0.00057245148521
< 0.1%
0.00058995469661
< 0.1%
0.00059059464961
< 0.1%
0.00059225646721
< 0.1%
0.00060169370761
< 0.1%
0.00060557947461
< 0.1%
0.00060676517031
< 0.1%
ValueCountFrequency (%)
0.0036649719641
< 0.1%
0.0035732405851
< 0.1%
0.0035636237121
< 0.1%
0.0035451815971
< 0.1%
0.0035064166321
< 0.1%
0.0034702313431
< 0.1%
0.0034348715811
< 0.1%
0.0034187812861
< 0.1%
0.003412362641
< 0.1%
0.0033926372321
< 0.1%

ShapeFactor3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6435901813
Minimum0.4103385841
Maximum0.9747671533
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:53.243023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4103385841
5-th percentile0.4768823776
Q10.5813585905
median0.6420443243
Q30.696006284
95-th percentile0.8262623662
Maximum0.9747671533
Range0.5644285692
Interquartile range (IQR)0.1146476935

Descriptive statistics

Standard deviation0.09899615048
Coefficient of variation (CV)0.1538186153
Kurtosis-0.1444750361
Mean0.6435901813
Median Absolute Deviation (MAD)0.05716184808
Skewness0.2424809267
Sum8759.905958
Variance0.00980023781
MonotonicityNot monotonic
2022-06-17T22:09:53.446114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52070244492
 
< 0.1%
0.52487916942
 
< 0.1%
0.47014328642
 
< 0.1%
0.49428073672
 
< 0.1%
0.53609979092
 
< 0.1%
0.47880052062
 
< 0.1%
0.45537811272
 
< 0.1%
0.48494017262
 
< 0.1%
0.56467062482
 
< 0.1%
0.49153254422
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.41033858411
< 0.1%
0.41649941461
< 0.1%
0.41705294191
< 0.1%
0.41804244441
< 0.1%
0.42089208071
< 0.1%
0.42125123921
< 0.1%
0.42144697981
< 0.1%
0.42462270341
< 0.1%
0.42493577451
< 0.1%
0.42529703751
< 0.1%
ValueCountFrequency (%)
0.97476715331
< 0.1%
0.96356043541
< 0.1%
0.9592870271
< 0.1%
0.95401876251
< 0.1%
0.95237058181
< 0.1%
0.95178464231
< 0.1%
0.94190038091
< 0.1%
0.94181480351
< 0.1%
0.93844375311
< 0.1%
0.93788376931
< 0.1%

ShapeFactor4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13543
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9950633118
Minimum0.9476874027
Maximum0.99973253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.5 KiB
2022-06-17T22:09:53.664845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.9476874027
5-th percentile0.9869830971
Q10.9937028955
median0.9963859377
Q30.997882543
95-th percentile0.9989919809
Maximum0.99973253
Range0.05204512734
Interquartile range (IQR)0.004179647584

Descriptive statistics

Standard deviation0.004366457749
Coefficient of variation (CV)0.004388120531
Kurtosis13.03806713
Mean0.9950633118
Median Absolute Deviation (MAD)0.001817961642
Skewness-2.759482912
Sum13543.80674
Variance1.906595327 × 10-5
MonotonicityNot monotonic
2022-06-17T22:09:53.883579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99390479132
 
< 0.1%
0.96927920722
 
< 0.1%
0.99051984152
 
< 0.1%
0.99770793792
 
< 0.1%
0.9982283382
 
< 0.1%
0.99668655082
 
< 0.1%
0.99630142072
 
< 0.1%
0.994672
 
< 0.1%
0.97237907362
 
< 0.1%
0.99432024152
 
< 0.1%
Other values (13533)13591
99.9%
ValueCountFrequency (%)
0.94768740271
< 0.1%
0.9499903111
< 0.1%
0.95123883571
< 0.1%
0.95503248971
< 0.1%
0.95686685741
< 0.1%
0.9572326631
< 0.1%
0.95732510751
< 0.1%
0.95732755321
< 0.1%
0.95774770871
< 0.1%
0.95828482511
< 0.1%
ValueCountFrequency (%)
0.999732531
< 0.1%
0.99970910121
< 0.1%
0.99970733611
< 0.1%
0.99968052071
< 0.1%
0.99967429221
< 0.1%
0.9996704521
< 0.1%
0.99965952621
< 0.1%
0.99965227581
< 0.1%
0.99963701081
< 0.1%
0.9996253791
< 0.1%

Class
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size106.5 KiB
DERMASON
3546 
SIRA
2636 
SEKER
2027 
HOROZ
1928 
CALI
1630 
Other values (2)
1844 

Length

Max length8
Median length6
Mean length5.797884064
Min length4

Characters and Unicode

Total characters78915
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEKER
2nd rowSEKER
3rd rowSEKER
4th rowSEKER
5th rowSEKER

Common Values

ValueCountFrequency (%)
DERMASON3546
26.1%
SIRA2636
19.4%
SEKER2027
14.9%
HOROZ1928
14.2%
CALI1630
12.0%
BARBUNYA1322
 
9.7%
BOMBAY522
 
3.8%

Length

2022-06-17T22:09:54.071066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-17T22:09:54.274199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
dermason3546
26.1%
sira2636
19.4%
seker2027
14.9%
horoz1928
14.2%
cali1630
12.0%
barbunya1322
 
9.7%
bombay522
 
3.8%

Most occurring characters

ValueCountFrequency (%)
R11459
14.5%
A10978
13.9%
S8209
10.4%
O7924
10.0%
E7600
9.6%
N4868
 
6.2%
I4266
 
5.4%
M4068
 
5.2%
B3688
 
4.7%
D3546
 
4.5%
Other values (7)12309
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter78915
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R11459
14.5%
A10978
13.9%
S8209
10.4%
O7924
10.0%
E7600
9.6%
N4868
 
6.2%
I4266
 
5.4%
M4068
 
5.2%
B3688
 
4.7%
D3546
 
4.5%
Other values (7)12309
15.6%

Most occurring scripts

ValueCountFrequency (%)
Latin78915
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R11459
14.5%
A10978
13.9%
S8209
10.4%
O7924
10.0%
E7600
9.6%
N4868
 
6.2%
I4266
 
5.4%
M4068
 
5.2%
B3688
 
4.7%
D3546
 
4.5%
Other values (7)12309
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII78915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R11459
14.5%
A10978
13.9%
S8209
10.4%
O7924
10.0%
E7600
9.6%
N4868
 
6.2%
I4266
 
5.4%
M4068
 
5.2%
B3688
 
4.7%
D3546
 
4.5%
Other values (7)12309
15.6%

Interactions

2022-06-17T22:09:41.630661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:50.831457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:55.103296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.430988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.408560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:04.355436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.396037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:10.414832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:13.961336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.600846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.594945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:23.663296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:27.683060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:31.684022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:35.245031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:38.567896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:41.800009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:51.031694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:55.285311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.624498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.582573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:04.535293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.572276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:10.585554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:14.162736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.774768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.777017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:23.833758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:27.916089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:31.891902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:35.435549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:38.754338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:41.987789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:51.225055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:55.527511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.827581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.771977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:04.722197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.760987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:10.770986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:14.386583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.958453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.970824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:24.014668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:28.333918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:32.097288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:35.714365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:38.940616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:42.169470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:51.395138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:55.762501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.996136image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.949256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:04.909214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.939022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:11.007638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:14.733377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:18.157459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:21.154572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:24.188394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:28.611599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:32.316142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:35.950228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:39.125816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:42.364280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:51.571508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:56.001524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:59.173427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:02.129694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:21.732713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:24.742882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:39.712718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:42.955763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:02.677252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:15.776165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:21.912627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:24.908895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:29.774449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:12.517947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:19.280771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:22.316387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:25.278840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-17T22:09:43.623489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:52.647543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:57.283847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:00.299202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:03.245547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:06.258049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:09.276954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:12.789063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:16.415370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:19.477824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:22.511627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:25.472643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:30.391191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:33.942586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:37.385052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:40.481360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:43.858105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:52.828574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:57.466737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:00.475518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:03.418341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:06.443724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:09.452759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:12.977269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:16.608293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:19.664221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:22.692534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:25.646820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:30.604113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:34.150261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:37.583869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:40.664339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:44.106945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:53.009129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:57.674366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:00.667815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:03.610142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:06.635342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:09.649482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:13.161937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:16.804344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:19.854935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:22.885630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:26.736522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:30.825435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:34.415793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:37.783760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:40.865989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:44.363470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:53.197116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:57.871128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:00.853429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:03.807628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:06.828644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:09.854721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:13.357317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.012702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.047269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:23.081230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:26.923328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:31.047748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:34.626660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:37.978106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:41.065786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:44.663969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:53.375738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.060040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.032351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:03.997532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.026973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:10.045272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:13.607912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.209196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.232243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:23.274331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:27.195593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:31.264304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:34.836562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:38.173494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:41.260881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:44.852432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:54.910342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:08:58.242933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:01.222428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:04.177870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:07.214886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:10.237195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:13.786433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:17.405893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:20.414573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:23.476795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:27.453614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:31.455867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:35.045003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:38.368927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-17T22:09:41.451569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-06-17T22:09:54.477307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-17T22:09:54.805408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-17T22:09:55.117885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-17T22:09:55.445966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-17T22:09:45.226537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-17T22:09:45.716030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class
028395610.291208.178117173.8887471.1971910.54981228715190.1410970.7639230.9888560.9580270.9133580.0073320.0031470.8342220.998724SEKER
128734638.018200.524796182.7344191.0973560.41178529172191.2727500.7839680.9849860.8870340.9538610.0069790.0035640.9098510.998430SEKER
229380624.110212.826130175.9311431.2097130.56272729690193.4109040.7781130.9895590.9478490.9087740.0072440.0030480.8258710.999066SEKER
330008645.884210.557999182.5165161.1536380.49861630724195.4670620.7826810.9766960.9039360.9283290.0070170.0032150.8617940.994199SEKER
430140620.134201.847882190.2792791.0607980.33368030417195.8965030.7730980.9908930.9848770.9705160.0066970.0036650.9419000.999166SEKER
530279634.927212.560556181.5101821.1710670.52040130600196.3477020.7756880.9895100.9438520.9237260.0070200.0031530.8532700.999236SEKER
630477670.033211.050155184.0390501.1467680.48947830970196.9886330.7624020.9840810.8530800.9333740.0069250.0032420.8711860.999049SEKER
730519629.727212.996755182.7372041.1655910.51376030847197.1243200.7706820.9893670.9671090.9254800.0069790.0031580.8565140.998345SEKER
830685635.681213.534145183.1571461.1658520.51408131044197.6596960.7715610.9884360.9542400.9256580.0069590.0031520.8568440.998953SEKER
930834631.934217.227813180.8974691.2008340.55364231120198.1390120.7836830.9908100.9702780.9121250.0070450.0030080.8319730.999061SEKER

Last rows

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class
1360142042771.515288.082674186.3470901.5459470.76261542476231.3645110.8162540.9897820.8875740.8031180.0068520.0017580.6449990.997134DERMASON
1360242047768.936292.975007183.1391411.5997400.78054342446231.3782690.7738900.9906000.8936440.7897540.0069680.0016720.6237120.997777DERMASON
1360342049770.185290.163403185.0516851.5680130.77024342503231.3837710.7560050.9893180.8907900.7974260.0069010.0017210.6358880.997080DERMASON
1360442070763.489289.022373186.1234341.5528530.76504642556231.4415430.7688230.9885800.9069360.8007740.0068700.0017430.6412390.995750DERMASON
1360542070760.701276.691651193.9453661.4266470.71321642458231.4415430.7308130.9908620.9135960.8364600.0065770.0019860.6996660.998176DERMASON
1360642097759.696288.721612185.9447051.5527280.76500242508231.5157990.7145740.9903310.9166030.8018650.0068580.0017490.6429880.998385DERMASON
1360742101757.499281.576392190.7131361.4764390.73570242494231.5267980.7999430.9907520.9220150.8222520.0066880.0018860.6760990.998219DERMASON
1360842139759.321281.539928191.1879791.4725820.73406542569231.6312610.7299320.9898990.9184240.8227300.0066810.0018880.6768840.996767DERMASON
1360942147763.779283.382636190.2757311.4893260.74105542667231.6532480.7053890.9878130.9079060.8174570.0067240.0018520.6682370.995222DERMASON
1361042159772.237295.142741182.2047161.6198410.78669342600231.6862230.7889620.9896480.8883800.7849970.0070010.0016400.6162210.998180DERMASON

Duplicate rows

Most frequently occurring

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4Class# duplicates
033518702.956277.571399154.3055811.7988420.83124034023206.5827750.8083830.9851570.8523770.7442510.0082810.0015670.5539090.996396HOROZ2
133954716.750277.368480156.3563261.7739510.82597034420207.9220420.7994820.9864610.8305490.7496240.0081690.0015910.5619360.996847HOROZ2
238427756.323306.533886160.5917841.9087770.85178238773221.1939780.7969760.9910760.8441740.7215970.0079770.0013340.5207020.993905HOROZ2
338891791.343319.499996156.8696192.0367230.87116839651222.5254120.6500250.9808330.7804220.6964800.0082150.0011920.4850850.987983HOROZ2
440804790.802323.475648163.2877171.9810160.86324141636227.9325920.7875700.9800170.8199310.7046360.0079280.0012060.4965120.983598HOROZ2
541978821.864337.171464160.0360672.1068470.88017842593231.1883420.6848850.9855610.7809650.6856700.0080320.0010950.4701430.990520HOROZ2
642156815.245335.198243160.9369382.0827920.87720042586231.6779800.8340460.9899030.7970640.6911670.0079510.0011190.4777120.994975HOROZ2
742450828.116347.951525156.4693662.2237680.89318642820232.4844480.6093880.9913590.7778670.6681520.0081970.0010080.4464270.992750HOROZ2
843099815.390328.234078168.6101161.9467050.85797743710234.2548850.6976660.9860220.8146040.7136820.0076160.0012190.5093430.991539HOROZ2
943746836.693339.352567165.4114422.0515660.87316144442236.0066460.7137780.9843390.7852640.6954620.0077570.0011190.4836670.992274HOROZ2